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Abstract

Assistive devices and technologies are getting common and some commercial products are starting to be available. However, the deployment of robots able to physically interact with a person in an assistive manner is still a challenging problem. Apart from the design and control, the robot must be able to adapt to the user it is attending in order to become a useful tool for caregivers. This robot behavior adaptation comes through the definition of user preferences for the task such that the robot can act in the user’s desired way. This article presents a taxonomy of user preferences for assistive scenarios, including physical interactions, that may be used to improve robot decision-making algorithms. The taxonomy categorizes the preferences based on their semantics and possible uses. We propose the categorization in two levels of application (global and specific) as well as two types (primary and modifier). Examples of real preference classifications are presented in three assistive tasks: feeding, shoe fitting and coat dressing.